zhangyue19921010 commented on code in PR #12884: URL: https://github.com/apache/hudi/pull/12884#discussion_r1986612432
########## rfc/rfc-89/rfc-89.md: ########## @@ -0,0 +1,379 @@ +<!-- + Licensed to the Apache Software Foundation (ASF) under one or more + contributor license agreements. See the NOTICE file distributed with + this work for additional information regarding copyright ownership. + The ASF licenses this file to You under the Apache License, Version 2.0 + (the "License"); you may not use this file except in compliance with + the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +--> +# RFC-89: Partition Level Bucket Index + +## Proposers +- @zhangyue19921010 + +## Approvers +- @danny0405 +- @xiarixiaoyao +- @LinMingQiang + +## Status + +JIRA: https://issues.apache.org/jira/browse/HUDI-8990 + +## Abstract + +As we know, Hudi proposed and introduced Bucket Index in RFC-29. Bucket Index can well unify the indexes of Flink and +Spark, that is, Spark and Flink could upsert the same Hudi table using bucket index. + +However, Bucket Index has a limit of fixed number of buckets. In order to solve this problem, RFC-42 proposed the ability +of consistent hashing achieving bucket resizing by splitting or merging several local buckets dynamically. + +But from PRD experience, sometimes a Partition-Level Bucket Index and a offline way to do bucket rescale is good enough +without introducing additional efforts (multiple writes, clustering, automatic resizing,etc.). Because the more complex +the Architecture, the more error-prone it is and the greater operation and maintenance pressure. + +In this regard, we could upgrade the traditional Bucket Index to implement a Partition-Level Bucket Index, so that users +can set a specific number of buckets for different partitions through a rule engine (such as regular expression matching). +On the other hand, for a certain existing partitions, an offline command is provided to reorganize the data using insert +overwrite(need to stop the data writing of the current partition). + +More importantly, the existing Bucket Index table can be upgraded to Partition-Level Bucket Index smoothly and seamlessly. + +## Background +The following is the core read-write process of the Flink/Spark engine based on Simple Bucket Index +### Flink Write Using Simple Bucket Index +**Step 1**: repartition input records based on `BucketIndexPartitioner`, BucketIndexPartitioner has **a fixed bucketNumber** for all partition path. +For each record key, compute a fixed data partition number and dispatch the record to its corresponding partition. + +```java +/** + * Bucket index input partitioner. + * The fields to hash can be a subset of the primary key fields. + * + * @param <T> The type of obj to hash + */ +public class BucketIndexPartitioner<T extends HoodieKey> implements Partitioner<T> { + + private final int bucketNum; + private final String indexKeyFields; + + private Functions.Function2<String, Integer, Integer> partitionIndexFunc; + + public BucketIndexPartitioner(int bucketNum, String indexKeyFields) { + this.bucketNum = bucketNum; + this.indexKeyFields = indexKeyFields; + } + + @Override + public int partition(HoodieKey key, int numPartitions) { + if (this.partitionIndexFunc == null) { + this.partitionIndexFunc = BucketIndexUtil.getPartitionIndexFunc(bucketNum, numPartitions); + } + int curBucket = BucketIdentifier.getBucketId(key.getRecordKey(), indexKeyFields, bucketNum); + return this.partitionIndexFunc.apply(key.getPartitionPath(), curBucket); + } +} +``` +**Step 2**: Using `BucketStreamWriteFunction` upsert records into hoodie +- Bootstrap and cache `partition_bucket -> fileID` mapping from the existing hudi table +- Tagging: compute `bucketNum` and tag `fileID` based on record key and bucketNumber config through `BucketIdentifier` +- buffer and write records + +### Flink Read Pruning Using Simple Bucket Index +**Step 1**: compute `dataBucket` +```java + private int getDataBucket(List<ResolvedExpression> dataFilters) { + if (!OptionsResolver.isBucketIndexType(conf) || dataFilters.isEmpty()) { + return PrimaryKeyPruners.BUCKET_ID_NO_PRUNING; + } + Set<String> indexKeyFields = Arrays.stream(OptionsResolver.getIndexKeys(conf)).collect(Collectors.toSet()); + List<ResolvedExpression> indexKeyFilters = dataFilters.stream().filter(expr -> ExpressionUtils.isEqualsLitExpr(expr, indexKeyFields)).collect(Collectors.toList()); + if (!ExpressionUtils.isFilteringByAllFields(indexKeyFilters, indexKeyFields)) { + return PrimaryKeyPruners.BUCKET_ID_NO_PRUNING; + } + return PrimaryKeyPruners.getBucketId(indexKeyFilters, conf); + } +``` +**Step 2**: Do partition pruning and get all files in given partitions +**Step 3**: do bucket pruning for all files from step2 +```java + /** + * Returns all the file statuses under the table base path. + */ + public List<StoragePathInfo> getFilesInPartitions() { + ... + // Partition pruning + String[] partitions = + getOrBuildPartitionPaths().stream().map(p -> fullPartitionPath(path, p)).toArray(String[]::new); + if (partitions.length < 1) { + return Collections.emptyList(); + } + List<StoragePathInfo> allFiles = ... + + // bucket pruning + if (this.dataBucket >= 0) { + String bucketIdStr = BucketIdentifier.bucketIdStr(this.dataBucket); + List<StoragePathInfo> filesAfterBucketPruning = allFiles.stream() + .filter(fileInfo -> fileInfo.getPath().getName().contains(bucketIdStr)) + .collect(Collectors.toList()); + logPruningMsg(allFiles.size(), filesAfterBucketPruning.size(), "bucket pruning"); + allFiles = filesAfterBucketPruning; + } + ... + } + +``` + +### Spark Write/Read Using Simple Bucket Index +The read-write process of Spark based on Bucket Index is also similar. +- Use `HoodieSimpleBucketIndex` to tag location. +- Use `SparkBucketIndexPartitioner` to packs incoming records to be inserted into buckets (1 bucket = 1 RDD partition). +- Use `BucketIndexSupport` to Bucket Index pruning during reading. + +## Design +### Overview +Implement a partition-level Bucket Index capability where users can set the calculation expression for the partition +bucket number via `hoodie.bucket.index.partition.expressions`. For historical partitions, provide a CALL command `call partitionBucketIndexManager(...)` +to support bucket rescaling which is a replace-commit. + +Note: When users invoke the CALL command to modify the expression and resize historical partitions' bucket number, +they must follow THREE STEPS +1. Firstly, stop all ingestion Jobs +2. Then, trigger call command and wait for bucket rescaling completed +3. Finally, restart all ingestion Jobs + +This STEPS ensures that all writers load the same expression config, maintaining consistency between the expression and the data. + + + +Next will introduce the implementation details. + +### New Config + +| Config | type | default | Description | +|-------------------------------------------|--------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| hoodie.bucket.index.partition.rule.type | string | regex | Set rule parser for expressions. | +| hoodie.bucket.index.partition.expressions | string | null | Users can use this parameter to specify expression and the corresponding bucket numbers (separated by commas).Multiple rules are separated by semicolons like `hoodie.bucket.index.partition.expressions=expression1,bucket-number1;expression2,bucket-number2` | +| hoodie.bucket.index.num.buckets | int | 4 | Hudi bucket number per partition. | + +The above parameters are at table-level which need to be declared in the DDL. Users can't change this config through +runtime options like Flink /*+ OPTIONS(...) */ + +```sql +CREATE TABLE hudi_table( + id BIGINT, + name STRING, + price DOUBLE +) WITH ( +'connector' = 'hudi', +'path' = 'file:///tmp/hudi_table', +'table.type' = 'MERGE_ON_READ', +'index.type' = 'BUCKET', +'hoodie.bucket.index.partition.expressions' = '\\d{4}-(06-(01|17|18)|11-(01|10|11)),256', +'hoodie.bucket.index.num.buckets' = '10' +) +``` + +For the dates of 06-01, 06-17, 06-18 in June and 01-11, 10-11, 11-11 in +November of each year (in the format of yyyy-MM-dd), the corresponding bucket number for the partition is 256 + +For common partitions use 10 as partition bucket number + +### Hashing Config And Management + +The expression config will be persisted as `<instant>.hashing_config` for current table, it stores in +`.hoodie/.hashing_meta` directory and contains the following information in json format + +```json +{ + "rule": "rule-engine", + "expressions": "expression1,bucket-number1;expression2,bucket-number2", + "default_bucket_number": "default-bucket-number" +} +``` +#### Multi-version for <instant>.hashing_config + +Apache Hudi natively supports multi-versioning and version rollback capabilities. For Bucket Rescale operation, +we must also support Bucket Rescale rollbacks, even rolling back multiple completed Bucket Rescale operations at once. +This requires tracking the instant and corresponding config version for each Bucket Rescale action. +During rollback, both the data and config must be reverted to ensure consistency between the configuration and the data state. + +So that we need to maintain config versions alongside commit instants which is multiple versions of hashing_config (multi-versioned configs). +This necessitates implementing mechanisms for config creation, update, rollback, cleanup and loading + +About Hashing_Config Creation + +The initial version *must and can only* be created via DDL. During the DDL phase, hoodie will create a +`00000000000000000.hashing_config` for the first time. + +About Hashing_Config Update + +A new version can only be updated via `call partitionBucketIndexManager(overwrite => 'new-expressions', dry-run => 'false')`, which is +a replace-commit operation. + +The transactional relationship between the replace-commit operation and hashing_config updates will be explained in the +PartitionBucketIndexManager section later. + +*Note:* Before updating the hashing_config, users must manually stop all ingestion jobs. + +```text +Firstly DDL phase creates initial version +ls .hoodie/.hashing_meta/ +.hoodie/.hashing_meta/00000000000000000.hashing_config +{ + "rule": "rule-engine", + "expressions": "expression1,bucket-number1;expression2,bucket-number2", + "default_bucket_number": "default-bucket-number" +} + +Secondly call PartitionBucketIndexManager(overwrite => 'expression3,bucket-number3;expression1,bucket-number1;expression2,bucket-number2', 'dry-run' = 'false') +ls .hoodie/.hashing_meta/ +.hoodie/.hashing_meta/00000000000000000.hashing_config (deprecated) +.hoodie/.hashing_meta/20250303095546020.hashing_config (used) +{ + "rule": "rule-engine", + "expressions": "expression3,bucket-number3;expression1,bucket-number1;expression2,bucket-number2", + "default_bucket_number": "default-bucket-number" +} +``` + +The expression written at the front has a higher priority, and the expression has a higher priority than the default_bucket_number. + +About Hashing_Config Rollback + +The Bucket Rescale operation is essentially an insert overwrite action and can seamlessly integrate with Hudi's rollback mechanism. +One simplification here is that when rolling back a Bucket Rescale, the corresponding replace-commit.hashing_config is not immediately deleted. +Instead, its cleanup is deferred to the version cleanup process (discussed later). + +This approach achieves two goals: +1. Minimizes modifications to the rollback service logic. +2. Lazy cleanup does not impact config loading, as Hudi always loads the latest hashing_config associated with committed replace commit. Review Comment: Updated. We will rollback hashing config during rollback insert overwrite(Bucket rescale action) -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
